Large Language Models (LLMs) demonstrate impressive capabilities across various fields,
yet their increasing use raises critical security concerns. This article reviews recent literature …

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the
development of Large Language Models (LLMs). While much of the current research in this …

The emergence of Large Language Models (LLMs) has revolutionized how users access
information, shifting from traditional search engines to direct question-and-answer …

We investigate the internal representations of vision-language models (VLMs) to address
hallucinations, a persistent challenge despite advances in model size and training. We …

The surge in applications of large language models (LLMs) has prompted concerns about
the generation of misleading or fabricated information, known as hallucinations. Therefore …

The hallucination problem of Large Language Models (LLMs) significantly limits their
reliability and trustworthiness. Humans have a self-awareness process that allows us to …

Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what
to retrieve during the text generation process of Large Language Models (LLMs). There are …

Statute retrieval aims to find relevant statutory articles for specific queries. This process is the
basis of a wide range of legal applications such as legal advice, automated judicial …

Evaluating the importance of different layers in large language models (LLMs) is crucial for
optimizing model performance and interpretability. This paper first explores layer importance …

LLM hallucination, where LLMs occasionally generate unfaithful text, poses significant
challenges for their practical applications. Most existing detection methods rely on external …